@article {4832, title = {What Is the Model in Model-Based Planning?}, journal = {Cognitive Science}, volume = {45}, year = {2021}, month = {01/2021}, abstract = {

Flexibility is one of the hallmarks of human problem-solving. In everyday life, people adapt to changes in common tasks with little to no additional training. Much of the existing work on flexibility in human problem-solving has focused on how people adapt to tasks in new domains by drawing on solutions from previously learned domains. In real-world tasks, however, humans must generalize across a wide range of within-domain variation. In this work we argue that representational abstraction plays an important role in such within-domain generalization. We then explore the nature of this representational abstraction in realistically complex tasks like video games by demonstrating how the same model-based planning framework produces distinct generalization behaviors under different classes of task representation. Finally, we compare the behavior of agents with these task representations to humans in a series of novel grid-based video game tasks. Our results provide evidence for the claim that within-domain flexibility in humans derives from task representations composed of propositional rules written in terms of objects and relational categories.

}, issn = {0364-0213}, doi = {10.1111/cogs.v45.110.1111/cogs.12928}, url = {https://onlinelibrary.wiley.com/toc/15516709/45/1}, author = {Pouncy, Thomas and Tsividis, Pedro and Samuel J Gershman} }